edgetpu
vgpu_unlock
edgetpu | vgpu_unlock | |
---|---|---|
34 | 144 | |
397 | 4,264 | |
3.8% | - | |
2.7 | 0.0 | |
over 2 years ago | about 1 year ago | |
C++ | C | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
edgetpu
- The Pixel 8 Pro's Tensor G3 off-loads all generative AI tasks to the cloud
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Chromebook Plus: more performance and AI capabilities
I know the tensor power pixelbook was shutdown and I never heard the actual reason just a bunch of speculation about costs/profitability which is probably true.
It's a shame that there isn't more competition and development in the neural asic world to harness the power of llms/generative AI on a low power, cheap hardware platform like the pixelbook line. For someone that invented the TPU they have done a not so great job of ensuring it's commercialization and support. Both on the hardware and software side.
The coral edge tpu seemed to be the right high level idea but without proper execution.
https://github.com/google-coral/edgetpu/issues/668
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Show HN: RISC-V core written in 600 lines of C89
> even in the 80s I wanted an FPGA accelerators in every machine
Mostly unrelated, but I recently discovered that you can buy TPUs, right now, as a consumer product, from https://coral.ai.
The stock firmware already allows you to run these things so hard they overheat, which is amazing.
But yes, I also want FPGA accelerators.
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Another PCIe A+E card in place of wifi in M900 tiny
I'm looking at the coral.ai cards and they have a M.2 A+E card, same form factor as the wifi slot in the m900 tiny. Has anyone tried another card in that slot other than wifi?
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Sony backs Raspberry Pi with fresh funding, access to A.I. chips
Chips optimized to perform the type of calculations used for NN inference at high parallelism. A good example would be the google spinoff https://coral.ai/ (though their usecase is highly limited by sub-par software constraints)
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Any ML accelarator chips?
By no means an expert, but I have seen prototypes using a raspberry pi and a dongle from Coral Ai. They have PCIE and USB based modules.
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Is Google coral getting abandoned
Last news on https://coral.ai/ was on May 5 2022
Activity on the github project seems to have stopped. https://github.com/google-coral
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Ask HN: Worth it to buy 4x Nvidia Tesla K40 for AI?
https://coral.ai/
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How do you effectively test accuracy of your software product?
Your problem statement still needs more clarification. If the above applies, the best way is to evaluate your ML-based pattern matcher on high-level scenarios. One approach to speed up the evaluation is to lift and shift the execution of scenarios into cloud. Another approach is to use an AI accelerator, such as http://coral.ai or other.
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Cluster AIs - low cost (lower performance) super/minicomputing
You probably could but not with raspis. Maybe the TPUs they sell. https://coral.ai/
vgpu_unlock
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Tinygrad: Hacked 4090 driver to enable P2P
This isnβt even the first time a hacked driver has been used to unlock some HW feature - https://github.com/DualCoder/vgpu_unlock
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Is there specific hardware to make passthrough GPU easier?
Alternatively enable vGPU for the 2070 and use it for both Jellyfin LXC and Windows VM. https://github.com/DualCoder/vgpu_unlock
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GPU virtualization?
I'm on Linux and I'm running a 3070 Ti (Nvidia). I have always wanted to do GPU virtualization but because NVIDIA won't release vGPU for consumer card no one can do it without crossing legal red tape or problems with bricking your GPU. I did find this [https://github.com/jamesstringerparsec/Easy-GPU-PV] however it is only for windows, I found this [https://github.com/Arc-Compute/LibVF.IO/] and does not work with my GPU, and this [https://github.com/DualCoder/vgpu_unlock] and can't get it to work. Done any one know an alternative on Linux that work just like this, overcoming these problems (on KVM)?
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GPU pass-through/Sharing between multiple VMs
Otherwise, your only other option is the real hardware virtualization options that are available. NVIDIA's enterprise vGPU solution is for expensive compute cards however some have had good luck making vGPUs work on consumer NVIDIA cards with tools such as vgpu_unlock
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SR-IOV with RTX 3090 Ti
There was a hack to enable it on some consumer cards, but itβs not available on Ampere/30x0 cards: https://github.com/DualCoder/vgpu_unlock/issues/8
- Gaming PC for Proxmox
- GPU virtualization, RTX 3000, Nvidia, and KVM?
- Hi, I need help building my VMware home-lab environment
- cheap gpu for virtualization and stable diffusion
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GPU Passthrough
Pci passthrough https://github.com/mbilker/vgpu_unlock-rs https://github.com/DualCoder/vgpu_unlock https://github.com/DualCoder/vgpu_unlock/issues/91 https://gitlab.com/polloloco/vgpu-proxmox https://github.com/joeknock90/Single-GPU-Passthrough https://gitlab.com/YuriAlek/vfio#start-here https://www.reddit.com/r/homelab/comments/b5xpua/the_ultimate_beginners_guide_to_gpu_passthrough/ https://forum.level1techs.com/t/single-gpu-passthrough-with-proxmox/113282/2 https://forum.proxmox.com/threads/problem-with-gpu-passthrough.55918/
What are some alternatives?
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Easy-GPU-PV - A Project dedicated to making GPU Partitioning on Windows easier!
scrypted - Scrypted is a high performance home video integration and automation platform
nvidia-patch - This patch removes restriction on maximum number of simultaneous NVENC video encoding sessions imposed by Nvidia to consumer-grade GPUs.
frigate - NVR with realtime local object detection for IP cameras
LibVF.IO - A vendor neutral GPU multiplexing tool driven by VFIO & YAML.
PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
OSX-KVM - Run macOS on QEMU/KVM. With OpenCore + Monterey + Ventura + Sonoma support now! Only commercial (paid) support is available now to avoid spammy issues. No Mac system is required.
yolov7_d2 - π₯π₯π₯π₯ (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! π₯π₯π₯
vgpu_unlock-rs - Unlock vGPU functionality for consumer grade GPUs
Dual-Edge-TPU-Adapter - Dual Edge TPU Adapter to use it on a system with single PCIe port on m.2 A/B/E/M slot
vga-passthrough - Up to date (2021) reference for setting up a VGA passthrough on (Ubuntu) Linux.